This topic describes the Scatter Plot component provided by Machine Learning Studio.

In regression analysis, a scatter plot shows the distribution of data points in a Cartesian coordinate system.

Configure the component

You can configure the component by using one of the following methods:
  • Machine Learning Platform for AI console
    Parameter Description
    Feature Columns Columns that are used to indicate sample data features
    Label Column The label column
    Samples The number of samples
  • PAI command
    PAI -name scatter_diagram -project algo_public
 -DselectedCols=emp_var_rate,cons_price_rate,cons_conf_idx,euribor3m
 -DlabelCol=y
 -DmapTable=pai_temp_2447_22859_2
 -DinputTable=scatter_diagram
 -DoutputTable=pai_temp_2447_22859_1;
    Parameter Required Description Default value
    inputTable Yes The name of the input table. No default value
    inputTablePartitions No The partitions selected from the input table for training. The system supports the following formats:
    • Partition_name=value
    • name1=value1/name2=value2: multi-level partitions
    Note If you specify multiple partitions, separate them with commas (,).
    		No default value
    outputTable Yes The name of the output table. No default value
    mapTable Yes The name of the output table that stores the maximum value, minimum value, and enumeration value of each feature. No default value
    selectedCols Yes The columns selected from the input table and used to draw a scatter plot. A maximum of five columns can be selected. No default value
    labelCol Yes An INT or STRING field is used as an enumeration label. No default value
    lifecycle Yes The lifecycle of the output table. Unit: days. 28

Example

  • Input data
    create table scatter_diagram as select emp_var_rate,cons_price_rate, cons_conf_idx,euribor3m,y from pai_bank_data limit 10
    emp_var_rate cons_price_rate cons_conf_idx euribor3m y
    1.4 93.918 -42.7 4.962 0
    -0.1 93.2 -42.0 4.021 0
    -1.7 94.055 -39.8 0.729 1
    -1.8 93.075 -47.1 1.405 0
    -2.9 92.201 31.4 0.869 1
    1.4 93.918 -42.7 4.961 0
    -1.8 92.893 -46.2 1.327 0
    -1.8 92.893 92.893 1.313 0
    -2.9 92.963 -40.8 1.266 1
    -1.8 93.075 -47.1 1.41 0
    1.1 93.994 -36.4 4.864 0
    1.4 93.444 -36.1 4.964 0
    1.4 93.444 -36.1 4.965 1
    -1.8 92.893 -46.2 1.291 0
    1.4 94.465 -41.8 4.96 0
    1.4 93.918 -42.7 4.962 0
    -1.8 93.075 -47.1 1.365 1
    -0.1 93.798 -40.4 4.86 1
    1.1 93.994 -36.4 4.86 0
    1.4 93.918 -42.7 4.96 0
    -1.8 93.075 -47.1 1.405 0
    1.4 94.465 -41.8 4.967 0
    1.4 93.918 -42.7 4.963 0
    1.4 93.918 -42.7 4.968 0
    1.4 93.918 -42.7 4.962 0
    -1.8 92.893 -46.2 1.344 0
    -3.4 92.431 -26.9 0.754 0
    -1.8 93.075 -47.1 1.365 0
    -1.8 92.893 -46.2 1.313 0
    1.4 93.918 -42.7 4.961 0
    1.4 94.465 -41.8 4.961 0
    -1.8 92.893 -46.2 1.327 0
    -1.8 92.893 -46.2 1.299 0
    -2.9 92.963 -40.8 1.268 1
    1.4 93.918 -42.7 4.963 0
    -1.8 92.893 -46.2 1.334 0
    1.4 93.918 -42.7 4.96 0
    -1.8 93.075 -47.1 1.405 0
    1.4 94.465 -41.8 4.96 0
    1.4 93.444 -36.1 4.962 0
    1.1 93.994 -36.4 4.86 0
    1.1 93.994 -36.4 4.857 0
    1.4 93.918 -42.7 4.961 0
    -3.4 92.649 -30.1 0.715 1
    1.4 93.444 -36.1 4.966 0
    -0.1 93.2 -42.0 4.076 0
    1.4 93.444 -36.1 4.965 0
    -1.8 92.893 -46.2 1.354 0
    1.4 93.444 -36.1 4.967 0
    1.4 94.465 -41.8 4.959 0
    -1.8 92.893 -46.2 1.354 0
    1.4 94.465 -41.8 4.958 0
    -1.8 92.893 -46.2 1.354 0
    1.4 94.465 -41.8 4.864 0
    1.1 93.994 -36.4 4.859 0
    1.1 93.994 -36.4 4.857 0
    -1.8 92.893 -46.2 1.27 0
    1.1 93.994 -36.4 4.857 0
    1.1 93.994 -36.4 4.859 0
    1.4 94.465 -41.8 4.959 0
    1.1 93.994 -36.4 4.856 0
    -1.8 93.075 -47.1 1.405 0
    -1.8 92.843 -50.0 1.811 1
    -0.1 93.2 -42.0 4.021 0
    -2.9 92.469 -33.6 1.029 0
    1.4 93.918 -42.7 4.962 0
    -1.8 93.075 -47.1 1.365 0
    1.1 93.994 -36.4 4.857 0
    -1.8 92.893 -46.2 1.259 0
    1.1 93.994 -36.4 4.857 0
    1.4 94.465 -41.8 4.866 0
    -2.9 92.201 -31.4 0.883 0
    -0.1 93.2 -42.0 4.076 0
    1.1 93.994 -36.4 4.857 0
    1.4 93.918 -42.7 4.96 0
    1.4 93.444 -36.1 4.962 0
    1.1 93.994 -36.4 4.858 0
    1.1 93.994 -36.4 4.857 0
    1.1 93.994 -36.4 4.856 0
    1.4 93.918 -42.7 4.968 0
    1.4 93.444 -36.1 4.966 0
    1.4 94.465 -41.8 4.962 0
    1.4 93.444 -36.1 4.963 0
    -1.8 92.843 -50.0 1.56 1
    1.4 93.918 -42.7 4.96 0
    1.4 93.444 -36.1 4.963 0
    -3.4 92.431 -26.9 0.74 0
    1.1 93.994 -36.4 4.856 0
    1.4 93.918 -42.7 4.962 0
    1.1 93.994 -36.4 4.856 0
    -0.1 93.2 -42.0 4.245 1
    1.1 93.994 -36.4 4.857 0
    -1.8 93.075 -47.1 1.405 0
    -1.8 92.893 -46.2 1.327 0
    -0.1 93.2 -42.0 4.12 0
    1.4 94.465 -41.8 4.958 0
    -1.8 93.749 -34.6 0.659 1
    1.1 93.994 -36.4 4.858 0
    1.1 93.994 -36.4 4.858 0
    1.4 93.444 -36.1 4.963 0
  • Parameter configuration

    Select the y column as the optional label column for the scatter plot. Select the select emp_var_rate, cons_price_rate, cons_conf_idx, and euribor3m columns as feature columns.

  • Output

    You can view the distribution of the objects specified by the label column for different features in the scatter plot.

    Scatter plot